Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression

Löhdefink, Jonas; Sitzmann, Jonas; Bär, Andreas; Fingscheidt, Tim

doi:10.1109/cvprw56347.2022.00181

Cited by 2 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al [48] proposed to incorporate trellis coded quantization. Löhdefink et al [49] proposed a one-hot max quantization. Although these methods showed better performance at low rates such as 0.1 bits per pixel (BPP), they perform worse at rates higher than 0.45 BPP.…”

Section: B Quantizer In Deep Image Compressionmentioning

confidence: 99%

Comprehensive Comparisons of Uniform Quantization in Deep Image Compression

Tsubota

Aizawa

2023

IEEE Access

View full text Add to dashboard Cite

In deep image compression, uniform quantization is applied to latent representations obtained by using an auto-encoder architecture for reducing bits and entropy coding. Quantization is a problem encountered in the end-to-end training of deep image compression. Quantization's gradient is zero, and it cannot backpropagate meaningful gradients. Many methods have been proposed to address the approximations of quantization to obtain gradients. However, there have not been equitable comparisons among them. In this study, we comprehensively compare the existing approximations of uniform quantization. Furthermore, we evaluate possible combinations of quantizers for the decoder and the entropy model, as the approximated quantizers can be different for them. We conduct experiments using three network architectures on two test datasets. The experimental results reveal that the best approximated quantization differs by the network architectures, and the best approximations of the three are different from the original ones used for the architectures. We also show that the combination of quantizers that uses universal quantization for the entropy model and differentiable soft quantization for the decoder is a comparatively good choice for different architectures and datasets.INDEX TERMS Image compression, neural networks, quantization.

show abstract

Section: B Quantizer In Deep Image Compressionmentioning

confidence: 99%

Comprehensive Comparisons of Uniform Quantization in Deep Image Compression

Tsubota

Aizawa

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The Industrial Internet of Things (IIoT) is the primary impetus behind the development of the 6th generation (6G) mobile communications infrastructure [1]. A new paradigm will be presented by an artificial intelligence (AI) supported 6G network, which will promote the inclusion of vertical industry in various scenarios such as industrial digital transformation and smart manufacturing [2].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Kumar¹,

Srinivasan²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image compression in the sensor, on the other hand, expedites the transfer of data while simultaneously reducing bandwidth use. The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties. The image encryption model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system. This helps to ensure that image datasets are kept safe. The Linde-Buzo-Gray (LBG) methodology is an example of a vector quantization algorithm that is extensively used and a relatively new form of picture reduction known as vector quantization (VQ). As a result, the purpose of this research is to create an artificial humming bird optimization approach that combines LBG-enabled codebook creation and encryption (AHBO-LBGCCE) for use in an IIoT setting. In the beginning, the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ. The Burrows-Wheeler Transform (BWT) model is used in order to accomplish codebook compression. In addition, the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained. A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms. The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

show abstract

Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression

Cited by 2 publications

References 20 publications

Comprehensive Comparisons of Uniform Quantization in Deep Image Compression

Comprehensive Comparisons of Uniform Quantization in Deep Image Compression

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Contact Info

Product

Resources

About